Showing papers on "Flow shop scheduling published in 2006"
01 Jan 2006
TL;DR: This survey provides a review of the subject of Grid scheduling mainly from the perspective of scheduling algorithms, and identifies the challenges and state of the art of current research.
Abstract: Thanks to advances in wide-area network technologies and the low cost of computing resources, Grid computing came into being and is currently an active research area. One motivation of Grid computing is to aggregate the power of widely distributed resources, and provide non-trivial services to users. To achieve this goal, an efficient Grid scheduling system is an essential part of the Grid. Rather than covering the whole Grid scheduling area, this survey provides a review of the subject mainly from the perspective of scheduling algorithms. In this review, the challenges for Grid scheduling are identified. First, the architecture of components involved in scheduling is briefly introduced to provide an intuitive image of the Grid scheduling process. Then various Grid scheduling algorithms are discussed from different points of view, such as static vs. dynamic policies, objective functions, applications models, adaptation, QoS constraints, strategies dealing with dynamic behavior of resources, and so on. Based on a comprehensive understanding of the challenges and the state of the art of current research, some general issues worthy of further exploration are proposed.
458 citations
TL;DR: This paper aims to provide a metaheuristic, in the form of a genetic algorithm, to a complex generalized flowshop scheduling problem that results from the addition of unrelated parallel machines at each stage, sequence dependent setup times and machine eligibility.
388 citations
TL;DR: This work proposes new genetic algorithms for solving the permutation FSP that prove to be competitive when compared to many other well known algorithms.
Abstract: The flowshop scheduling problem (FSP) has been widely studied in the literature and many techniques for its solution have been proposed. Some authors have concluded that genetic algorithms are not suitable for this hard, combinatorial problem unless hybridization is used. This work proposes new genetic algorithms for solving the permutation FSP that prove to be competitive when compared to many other well known algorithms. The optimization criterion considered is the minimization of the total completion time or makespan ( C max ). We show a robust genetic algorithm and a fast hybrid implementation. These algorithms use new genetic operators, advanced techniques like hybridization with local search and an efficient population initialization as well as a new generational scheme. A complete evaluation of the different parameters and operators of the algorithms by means of a Design of Experiments approach is also given. The algorithm's effectiveness is compared against 11 other methods, including genetic algorithms, tabu search, simulated annealing and other advanced and recent techniques. For the evaluations we use Taillard's well known standard benchmark. The results show that the proposed algorithms are very effective and at the same time are easy to implement.
368 citations
TL;DR: The novel method requires few control variables, is relatively easy to implement and use, effective, and efficient, which makes it an attractive and widely applicable approach for solving practical engineering problems.
324 citations
TL;DR: A hybrid particle swarm optimization (PSO) for the job shop problem (JSP) is proposed and the computational results show that the modified PSO performs better than the original design, and that the hybrid PSO is better than other traditional metaheuristics.
307 citations
TL;DR: An immune algorithm approach to the scheduling of a SDST hybrid flow shop is described and it was established that IA outperformed RKGA.
239 citations
TL;DR: A review of the literature related to the class of scheduling problems that involve sequence-dependent setup times (costs), an important consideration in many practical applications, can be found in this paper.
Abstract: This paper reviews the literature related to the class of scheduling problems that involve sequence-dependent setup times (costs), an important consideration in many practical applications. It focuses on papers published within the last decade, addressing a variety of machine configurations including single machine, parallel machine, flow shop, and job shop systems and reviews the optimization and heuristic solution methods used for each category. Since lot sizing is so intimately related to scheduling, this paper reviews work that integrates these issues in relationship to each configuration. This paper provides a perspective of this line of research, gives conclusions, and discusses fertile research opportunities posed by this class of scheduling problems.
229 citations
TL;DR: A logistics scheduling model for two processing centers that are located in different cities and can be modeled as a parallel-machine scheduling problem with transshipment between the machines.
Abstract: Logistics scheduling refers to problems in which decisions on job scheduling and transportation are integrated into a single framework. A logistics scheduling model for two processing centers that are located in different cities is presented. Each processing center has its own customers. When the demand in one processing center exceeds its processing capacity, it is possible to use part of the capacity of the other processing center subject to a job transshipment delay. Such a coordinated scheduling situation can be modeled as a parallel-machine scheduling problem with transshipment between the machines. We study problems with different objective functions and constraints, and propose various algorithms to solve these problems. Discussions on the benefits and incentives for the coordinated approach are presented.
222 citations
03 Dec 2006
TL;DR: A novel approach that uses the honey bees foraging model to solve the job shop scheduling problem and experimental results comparing the proposed honey bee colony approach with existing approaches such as ant colony and tabu search are presented.
Abstract: In the face of globalization and rapidly shrinking product life cycle, manufacturing companies are trying different means to improve productivity through management of machine utilization and product cycle-time. Job shop scheduling is an important task for manufacturing industry in terms of improving machine utilization and reducing cycle-time. However, job shop scheduling is inherently a NP-hard problem with no easy solution. This paper describes a novel approach that uses the honey bees foraging model to solve the job shop scheduling problem. Experimental results comparing the proposed honey bee colony approach with existing approaches such as ant colony and tabu search will be presented.
220 citations
TL;DR: A computationally efficient online scheduling algorithm, which can be seen as a compromise, is presented and its performance is evaluated and this algorithm, called optimal pointer placement (OPP) scheduling algorithm), is applied to the control and scheduling of a car suspension system.
Abstract: This brief addresses the problem of the optimal control and scheduling of networked control systems over limited bandwidth deterministic networks. Multivariable linear systems subject to communication constraints are modeled in the mixed logical dynamical (MLD) framework. The translation of the MLD model into the mixed integer quadratic programming (MIQP) formulation is described. This formulation allows the solving of the optimal control and scheduling problem using efficient branch and bound algorithms. Advantages and drawbacks of online and offline scheduling algorithms are discussed. Based on this discussion, a computationally efficient online scheduling algorithm, which can be seen as a compromise, is presented and its performance is evaluated. Finally, this algorithm, called optimal pointer placement (OPP) scheduling algorithm, is applied to the control and scheduling of a car suspension system.
217 citations
TL;DR: This paper models the risk and insecure conditions in grid job scheduling, and proposes six risk-resilient scheduling algorithms to assure secure grid job execution under different risky conditions that can upgrade grid performance significantly at only a moderate increase in extra resources or scheduling delays in a risky grid computing environment.
Abstract: In scheduling a large number of user jobs for parallel execution on an open-resource grid system, the jobs are subject to system failures or delays caused by infected hardware, software vulnerability, and distrusted security policy. This paper models the risk and insecure conditions in grid job scheduling. Three risk-resilient strategies, preemptive, replication, and delay-tolerant, are developed to provide security assurance. We propose six risk-resilient scheduling algorithms to assure secure grid job execution under different risky conditions. We report the simulated grid performances of these new grid job scheduling algorithms under the NAS and PSA workloads. The relative performance is measured by the total job makespan, grid resource utilization, job failure rate, slowdown ratio, replication overhead, etc. In addition to extending from known scheduling heuristics, we developed a new space-time genetic algorithm (STGA) based on faster searching and protected chromosome formation. Our simulation results suggest that, in a wide-area grid environment, it is more resilient for the global job scheduler to tolerate some job delays instead of resorting to preemption or replication or taking a risk on unreliable resources allocated. We find that delay-tolerant min-min and STGA job scheduling have 13-23 percent higher performance than using risky or preemptive or replicated algorithms. The resource overheads for replicated job scheduling are kept at a low 15 percent. The delayed job execution is optimized with a delay factor, which is 20 percent of the total makespan. A Kiviat graph is proposed for demonstrating the quality of grid computing services. These risk-resilient job scheduling schemes can upgrade grid performance significantly at only a moderate increase in extra resources or scheduling delays in a risky grid computing environment
TL;DR: This paper proposes a hybrid genetic algorithm to solve the flexible job shop scheduling problem with non-fixed availability constraints (fJSP-nfa) and defines two kinds of neighbourhood for the problem based on the concept of critical path.
Abstract: Most flexible job shop scheduling models assume that the machines are available all of the time. However, in most realistic situations, machines may be unavailable due to maintenances, pre-schedules and so on. In this paper, we study the flexible job shop scheduling problem with availability constraints. The availability constraints are non-fixed in that the completion time of the maintenance tasks is not fixed and has to be determined during the scheduling procedure. We then propose a hybrid genetic algorithm to solve the flexible job shop scheduling problem with non-fixed availability constraints (fJSP-nfa). The genetic algorithm uses an innovative representation method and applies genetic operations in phenotype space in order to enhance the inheritability. We also define two kinds of neighbourhood for the problem based on the concept of critical path. A local search procedure is then integrated under the framework of the genetic algorithm. Representative flexible job shop scheduling benchmark problems and fJSP-nfa problems are solved in order to test the effectiveness and efficiency of the suggested methodology.
TL;DR: This paper addresses the assembly flowshop scheduling problem with respect to a due date-based performance measure, i.e., maximum lateness, and proposes three heuristics for the problem: particle swarm optimization, Tabu search, and EDD.
TL;DR: Simulation runs are conducted to compare the performance of the proposed MAS-based IPPS approaches and that of an evolutionary algorithm and it is shown that the hybrid-based MAS, with the introduction of supervisory control, is able to provide integrated process plan and job shop scheduling solutions with a better global performance.
Abstract: This paper is on the development of an agent-based approach for the dynamic integration of the process planning and scheduling functions. In consideration of the alternative processes and alternative machines for the production of each part, the actual selection of the schedule and allocation of manufacturing resources is achieved through negotiation among the part and machine agents which represent the parts and manufacturing resources, respectively. The agents are to negotiate on a fictitious cost with the adoption of a currency function. Two MAS architectures are evaluated in this paper. One is a simple MAS architecture comprises part agents and machine agents only; the other one involves the addition of a supervisor agent to establish a hybrid-based MAS architecture. A hybrid contract net protocol is developed in the paper to support both types of MAS architectures. This new negotiation protocol enables multi-task many-to-many negotiations, it also incorporates global control into the decentralized negotiation. Simulation runs are conducted to compare the performance of the proposed MAS-based IPPS approaches and that of an evolutionary algorithm. It also shows that the hybrid-based MAS, with the introduction of supervisory control, is able to provide integrated process plan and job shop scheduling solutions with a better global performance.
TL;DR: In this article, the authors addressed multiobjective scheduling problems in a flexible manufacturing environment using evolutionary algorithms and made an attempt to consider simultaneously the machine and vehicle scheduling aspects in an FMS and addressed the combined problem for the minimization of makespan, mean flow time and mean tardiness objectives.
Abstract: A carefully designed and efficiently managed material handling system plays an important role in planning and operation of a flexible manufacturing system. Most of the researchers have addressed machine and vehicle scheduling as two independent problems and most of the research has been emphasized only on single objective optimization. Multiobjective problems in scheduling with conflicting objectives are more complex and combinatorial in nature and hardly have a unique solution. This paper addresses multiobjective scheduling problems in a flexible manufacturing environment using evolutionary algorithms. In this paper the authors made an attempt to consider simultaneously the machine and vehicle scheduling aspects in an FMS and addressed the combined problem for the minimization of makespan, mean flow time and mean tardiness objectives.
TL;DR: Through the improvement of the option modes of gBest and pBest of PSO algorithm, a similar particle swarm optimization algorithm (SPSOA) applied for permutation flowshop scheduling to minimize makespan is presented and it is obtained that the SPSOAs are more clearly efficacious than standard GAs for FSSP to minimizing makespan.
01 Dec 2006
TL;DR: This work proves a lower bound on the efficiency of a distributed scheduling algorithm by first assuming that all of the traffic only uses one hop of the network and proves that the lower bound is tight in the sense that, for any fraction larger than the lowerbound, it can find a topology and an arrival rate vector within the fraction of the capacity region such that the network is unstable under a greedy scheduling policy.
Abstract: We consider the problem of distributed scheduling in wireless networks subject to simple collision constraints. We define the efficiency of a distributed scheduling algorithm to be the largest number (fraction) such that the throughput under the distributed scheduling policy is at least equal to the efficiency multiplied by the maximum throughput achievable under a centralized policy. For a general interference model, we prove a lower bound on the efficiency of a distributed scheduling algorithm by first assuming that all of the traffic only uses one hop of the network. We also prove that the lower bound is tight in the sense that, for any fraction larger than the lower bound, we can find a topology and an arrival rate vector within the fraction of the capacity region such that the network is unstable under a greedy scheduling policy. We then extend our results to a more general multihop traffic scenario and show that similar scheduling efficiency results can be established by introducing prioritization or regulators to the basic greedy scheduling algorithm.
TL;DR: A brief review on job shop scheduling techniques in semiconductor manufacturing can be found in this paper, where the authors provide a brief overview of the problem, the techniques used and the researchers involved in solving this problem.
Abstract: This paper presents a brief review on job shop scheduling techniques in semiconductor manufacturing. The manufacturing environment in a semiconductor industry is considered a highly complex job shop, involving multiple types of work centers, large and changing varieties of products, sequence-dependent setup times, reentrant process flow, etc., in a dynamic scheduling environment. Due to the stubborn nature of the deterministic job shop scheduling problem itself, many of the solutions proposed are of hybrid construction cutting across the traditional disciplines. The problem has been investigated from a variety of perspectives resulting in several analytical techniques combining generic as well as problem-specific strategies. In this paper, we seek to provide a brief overview of the problem, the techniques used and the researchers involved in solving this problem.
TL;DR: A genetic algorithm with dominant genes (GADG) approach to deal with distributed flexible manufacturing system (FMS) scheduling problems subject to machine maintenance constraint is proposed.
Abstract: In general, distributed scheduling problem focuses on simultaneously solving two issues: (i) allocation of jobs to suitable factories and (ii) determination of the corresponding production scheduling in each factory. The objective of this approach is to maximize the system efficiency by finding an optimal planning for a better collaboration among various processes. This makes distributed scheduling problems more complicated than classical production scheduling ones. With the addition of alternative production routing, the problems are even more complicated. Conventionally, machines are usually assumed to be available without interruption during the production scheduling. Maintenance is not considered. However, every machine requires maintenance, and the maintenance policy directly affects the machine's availability. Consequently, it influences the production scheduling. In this connection, maintenance should be considered in distributed scheduling. The objective of this paper is to propose a genetic algorithm with dominant genes (GADG) approach to deal with distributed flexible manufacturing system (FMS) scheduling problems subject to machine maintenance constraint. The optimization performance of the proposed GADG will be compared with other existing approaches, such as simple genetic algorithms to demonstrate its reliability. The significance and benefits of considering maintenance in distributed scheduling will also be demonstrated by simulation runs on a sample problem.
30 Jul 2006
TL;DR: In this paper, the authors consider offline scheduling algorithms that incorporate speed scaling to address the bicriteria problem of minimizing energy consumption and a scheduling metric, and give linear-time algorithms to compute all non-dominated solutions for the general uniprocessor problem and for the multiprocessors problem when every job requires the same amount of work.
Abstract: We consider offline scheduling algorithms that incorporate speed scaling to address the bicriteria problem of minimizing energy consumption and a scheduling metric. For makespan, we give linear-time algorithms to compute all non-dominated solutions for the general uniprocessor problem and for the multiprocessor problem when every job requires the same amount of work. We also show that the multiprocessor problem becomes NP-hard when jobs can require different amounts of work.For total flow, we show that the optimal flow corresponding to a particular energy budget cannot be exactly computed on a machine supporting arithmetic and the extraction of roots. This hardness result holds even when scheduling equal-work jobs on a uniprocessor. We do, however, extend previous work by Pruhs et al. to give an arbitrarilygood approximation for scheduling equal-work jobs on a multiprocessor.
TL;DR: In this paper, an effective hybrid genetic algorithm (HGA) is proposed for permutation flow shop scheduling with limited buffers and a neighborhood structure based on graph model is employed to enhance the local search, so that the exploration and exploitation abilities can be well balanced.
TL;DR: This paper investigates the two-stage hybrid flow shop scheduling problem with only one machine on the first stage and m machines on the second stage to minimize the makespan and gives the Branch and Bound model for this problem.
TL;DR: It is shown that even with the introduction of deterioration and learning effect to job processing times, several single machine problems and several flow shop problems remain polynomially solvable, respectively.
Abstract: In this note, we consider the machine scheduling problems with the effects of deterioration and learning. In this model, job processing times are defined by functions of their starting times and positions in the sequence. The scheduling objectives are makespan (weighted) sum of completion times and maximum lateness. It is shown that even with the introduction of deterioration and learning effect to job processing times, several single machine problems and several flow shop problems remain polynomially solvable, respectively.
Book•
01 Jan 2006
TL;DR: A history of production scheduling can be found in this article, where the human factor in planning and scheduling is discussed and a review of long and short-term production scheduling at Lkab's Kiruna Mine is presented.
Abstract: A History of Production Scheduling- The Human Factor in Planning and Scheduling- Organizational, Systems and Human Issues in Production Planning, Scheduling and Control- Decision-Making Systems in Production Scheduling- Scheduling and Simulation- Rescheduling Strategies, Policies, and Methods- A Practical View of the Complexity in Developing Master Production Schedules: Fundamentals, Examples, and Implementation- Coordination Issues in Supply Chain Planning and Scheduling- Semiconductor Manufacturing Scheduling and Dispatching- The Slab Design Problem in the Steel Industry- A Review of Long- and Short-Term Production Scheduling at Lkab's Kiruna Mine- Scheduling Models for Optimizing Human Performance and Well-Being
TL;DR: A fast dynamic local search heuristic algorithm for the job-shop model suitable for considering one of the different performance criteria and embedding aircraft position shifting control technique to limit the controllers/pilots’ workload is proposed.
Abstract: We propose a job-shop scheduling model with sequence dependent set-up times and release dates to coordinate both inbound and outbound traffic flows on all the prefixed routes of an airport terminal area and all aircraft operations at the runway complex. The proposed model is suitable for representing several operational constraints (e.g., longitudinal and diagonal separations in specific airspace regions), and different runway configurations (e.g., crossing, parallel, with or without dependent approaches) in a uniform framework. The complexity and the highly dynamic nature of the problem call for heuristic approaches. We propose a fast dynamic local search heuristic algorithm for the job-shop model suitable for considering one of the different performance criteria and embedding aircraft position shifting control technique to limit the controllers/pilots' workload. Finally, we describe in detail the experimental analysis of the proposed model and algorithm applied to two real case studies of Milan-Malpensa and Rome-Fiumicino airport terminal areas.
TL;DR: This work introduces a model for non-preemptive scheduling under uncertainty, and proposes simple, combinatorial online scheduling policies for that model, and derives performance guarantees that match the currently best known performance guarantees for stochastic parallel machine scheduling.
Abstract: We consider a model for scheduling under uncertainty. In this model, we combine the main characteristics of online and stochastic scheduling in a simple and natural way. Job processing times are assumed to be stochastic, but in contrast to traditional stochastic scheduling models, we assume that jobs arrive online, and there is no knowledge about the jobs that will arrive in the future. The model incorporates both stochastic scheduling and online scheduling as a special case. The particular setting we consider is nonpreemptive parallel machine scheduling, with the objective to minimize the total weighted completion times of jobs. We analyze simple, combinatorial online scheduling policies for that model, and derive performance guarantees that match performance guarantees previously known for stochastic and online parallel machine scheduling, respectively. For processing times that follow new better than used in expectation (NBUE) distributions, we improve upon previously best-known performance bounds from stochastic scheduling, even though we consider a more general setting.
TL;DR: In this paper, the authors proposed an integrated formulation of the combined production and material handling scheduling problems, which is formulated as a mathematical programming model and as a constraint programming model which are compared for optimally solving a series of test problems.
10 Apr 2006
TL;DR: The use of genetic programming in automatized synthesis of scheduling heuristics for single machine dynamic problem and job shop scheduling with bottleneck estimation is investigated.
Abstract: This paper investigates the use of genetic programming in automatized synthesis of scheduling heuristics The applied scheduling technique is priority scheduling, where the next state of the system is determined based on priority values of certain system elements The evolved solutions are compared with existing scheduling heuristics for single machine dynamic problem and job shop scheduling with bottleneck estimation
TL;DR: In this paper, a novel ant colony system (ACS) heuristic is proposed to solve the hybrid flow shop scheduling problem (HFSP) with multiprocessor tasks, a core topic for numerous industrial applications.
Abstract: The hybrid flow-shop scheduling problem (HFSP) has been of continuing interest for researchers and practitioners since its advent. This paper considers the multistage HFSP with multiprocessor tasks, a core topic for numerous industrial applications. A novel ant colony system (ACS) heuristic is proposed to solve the problem. To verify the developed heuristic, computational experiments are conducted on two well-known benchmark problem sets and the results are compared with genetic algorithm (GA) and tabu search (TS) from the relevant literature. Computational results demonstrate that the proposed ACS heuristic outperforms the existing GA and TS algorithms for the current problem. Since the proposed ACS heuristic is comprehensible and effective, this study successfully develops a near-optimal approach which will hopefully encourage practitioners to apply it to real-world problems.
TL;DR: The use of a heuristic and a genetic algorithm for scheduling a multi-project environment with an objective to minimize the makespan of the projects is proposed.
Abstract: Managing multiple projects is a complex task. It involves the integration of varieties of resources and schedules. The researchers have proposed many tools and techniques for single project scheduling. Mathematical programming and heuristics are limited in application. In recent years non-traditional techniques are attempted for scheduling. This paper proposes the use of a heuristic and a genetic algorithm for scheduling a multi-project environment with an objective to minimize the makespan of the projects. The proposed method is validated with numerical examples and is found competent.